IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v55y2024i7p1304-1325.html
   My bibliography  Save this article

A dynamic programming approach for energy management in hybrid electric vehicles under uncertain driving conditions

Author

Listed:
  • Junpeng Deng
  • Massimo Tipaldi
  • Luigi Glielmo
  • Paolo Roberto Massenio
  • Luigi Del Re

Abstract

This paper addresses the limited adaptability and the computational burden of energy management systems (EMSs) for hybrid electric vehicles (HEVs) implemented via dynamic programming (DP)-based approaches. First, a deterministic dynamic programming (DDP) framework is presented to solve HEV EMS problems subject to a specific driving cycle. To address this limitation, an improved DDP approach, integrating the actual travelled position of the vehicle into the control law, is proposed. This way, a given DDP-based EMS can be applied to all the driving cycles, yet still measured on the same road. Stochastic dynamic programming (SDP)-based EMSs are also developed and prove to be more adaptive to driving scenarios completely different from the ones used for their computation. Real-world driving cycles are employed in all the presented cases, while a reduced HEV powertrain model is used to alleviate the typical DP computational burden.

Suggested Citation

  • Junpeng Deng & Massimo Tipaldi & Luigi Glielmo & Paolo Roberto Massenio & Luigi Del Re, 2024. "A dynamic programming approach for energy management in hybrid electric vehicles under uncertain driving conditions," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(7), pages 1304-1325, May.
    • Handle: RePEc:taf:tsysxx:v:55:y:2024:i:7:p:1304-1325
    DOI: 10.1080/00207721.2024.2304666
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2024.2304666
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2024.2304666?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tsysxx:v:55:y:2024:i:7:p:1304-1325. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.